The 1st Workshop on Trustworthy Learning on Graphs (TrustLOG)

Colocated with the 31st ACM International Conference on Information and Knowledge Management


Learning on graphs is at the core of many domains, ranging from information retrieval, social network analysis to transportation and computational chemistry. Years of research in this area have developed a wealth of theories, algorithms, and open-source systems for a variety of learning tasks. State-of-the-art graph learning models have been widely deployed in various real-world applications, often delivering superior empirical performance in answering what/who questions. For example, what are the most relevant web pages with respect to a user query? Who can be grouped into the same community? What items should we recommend to best-fit user preferences? Despite the prosperous development of high-utility graph learning models, recent studies reveal that learning on graphs is not trustworthy in many aspects. For example, existing methods make decisions in a black-box manner, which hinders the end-users to understand and trust model decisions. Many commonly applied approaches are also found to be vulnerable to malicious attacks, biased against individuals from certain demographic groups, or insecure to information leakage. As such, a fundamental question largely remains nascent: how can we make learning algorithms on graphs trustworthy? To answer this question, it is crucial to propose a paradigm shift, from answering what/who to understanding how/why, e.g., how the ranking of webpages can be manipulated by the malicious link farms; why two seemingly different users are grouped into the same online community; how sensitive the recommendation results are due to the random noises or fake ratings.

There are many challenges involved in trustworthy learning on graphs, including:

  • Understanding the implications of non-IID graph data on the classic trustworthy machine learning;
  • Discovering graph-specific measurements and techniques for trustworthy learning;
  • Achieving trustworthy learning on graphs at scale;
  • Accommodating the heterogeneity of graph data;
  • Dealing with dynamically changing and/or temporal graphs.

This one-day workshop aims to bring together researchers and practitioners from different backgrounds to answer these research questions and enhance the trustworthiness of learning on graphs. The workshop will consist of contributed talks, contributed posters, invited talks and discussion panels on a wide variety of methods and applications. Work-in-progress papers, demos, and visionary papers are also welcomed. We will also include invited papers for both oral presentation and poster presentation. This workshop intends to share visions of investigating new approaches and methods at the intersection of trustworthy learning on graphs and real-world applications.

Call for Papers


We invite submissions on a broad range of trustworthy learning on graphs. We welcome many types of papers, such as (but are not limited to):

  • Research papers
  • Work-in-progress papers
  • Demo papers
  • Visionary papers/white papers
  • Appraisal papers of existing methods or tools
  • Evaluatory papers on assumptions, methods or tools
  • Relevant work that will be or have been published

Topics of interests include (but are not limited to):

  • Safety and robustness
  • Interpretability, explainability and transparency
  • Ethics
  • Accountability
  • Privacy preservation
  • Causal analysis
  • Environmental well-being
  • Industry applications of trustworthy learning on graphs
  • Datasets and benchmarks for trustworthy learning on graphs

Important Dates

  • Recommended paper submission deadline: September 2, 2022(full consideration)
  • Dynamic submission window: September 3, 2022 ~ October 10, 2022(closed after pool is filled)
  • Reviews period: September 11 - September 25, 2022
  • Final notification: October 2, 2022
  • Camera-ready submission: October 15, 2022
  • Workshop day: October 21, 2022

Paper Submission

Paper submissions are limited to a total of 5 pages for initial submission(up to 6 pages for final camera-ready submission), plus references or supplementary materials, and authors should only rely on the supplementary material to include minor details that do not fit in the 5-page main body. Manuscripts should be submitted in PDF format, using the ACM 2-column sigconf template. Paper reviews will be double-blind, and submissions that are not properly anonymized will be desk-rejected without review. Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity of writing. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well executed, and repeatable. Authors are strongly encouraged to make data and code publicly available whenever possible. The accepted papers will be posted on the workshop website and will not be included in the CIKM proceedings. Special issues in flagship academic journals are under consideration to host the extended versions of best/selected papers in the workshop.

Please submit to CMT via this link

ACM Policy Against Discrimination

All authors and participants must adhere the the ACM discrimination policy. For full details, please visit this site.As a published ACM author, you and your co-authors are subject to all ACM Publications Policies , including ACM's new Publications Policy on Research Involving Human Participants and Subjects.

Agenda (Tentative)

Rows highlighted in green are LIVE whereas rows highlighted in yellow are PRE-RECORDED. The poster session is distributed across multiple ZOOM.

8:00am~8:15am Opening Session Livestream (above)
8:15am~9:15am Keynote speaker: Dr. Nitesh Chawla, University of Notre Dame TBD
9:15am~10:15am Keynote speaker: Dr. Stephan Günnemann, Technical University of Munich TBD
10:15am~10:30am Coffee break
10:30am~11:30am Keynote speaker: Dr. Marinka Zitnik, Harvard University TBD
11:30am~12:30am Keynote speaker: Dr. Haohan Wang, University of Illinois Urbana-Champaign TBD
Lunch Break
1:30pm~2:30pm Keynote speaker: Dr. Thomas Dietterich, Oregon State University TBD
2:30pm~3:30pm Keynote speaker: Dr. Yinglong Xia, Meta TBD
3:30pm~3:45pm Coffee break
3:45pm~4:45pm Keynote speaker: Dr. Shuiwang Ji, Texas A&M University TBD
4:45pm~5:00pm Best paper award ceremony + final remarks
5:00pm~6:00pm Poster Session

Keynote Speakers

Dr. Nitesh V. Chawla

Frank M. Freimann Professor, the University of Notre Dame

Nitesh V. Chawla is the Frank M. Freimann Professor of Computer Science and Engineering at the University of Notre Dame. He is the Founding Director of the Lucy Family Institute for Data and Society. He has also served as the director of the Center for Network and Data Science. He is a Fellow of IEEE. Chawla, who joined the Notre Dame faculty in 2007, is an expert in artificial intelligence, data science, and network science, and is motivated by the question of how technology can advance the common good through interdisciplinary research. As such, his research is not only at the frontier of fundamental methods and algorithms but is also making interdisciplinary advances through collaborations with faculty at Notre Dame and community, national, and international partners.

Dr. Thomas G. Dietterich

Distinguished Professor (Emeritus) and Director of Intelligent Systems, Oregon State University

Thomas G. Dietterich is Distinguished Emeritus Professor of computer science at Oregon State University. He is one of the pioneers of the field of machine learning. He served as executive editor of Machine Learning (journal) (1992–98) and helped co-found the Journal of Machine Learning Research. In response to the media's attention on the dangers of artificial intelligence, Dietterich has been quoted for an academic perspective to a broad range of media outlets including National Public Radio, Business Insider, Microsoft Research, CNET, and The Wall Street Journal. Among his research contributions were the invention of error-correcting output coding to multi-class classification, the formalization of the multiple-instance problem, the MAXQ framework for hierarchical reinforcement learning, and the development of methods for integrating non-parametric regression trees into probabilistic graphical models.

Dr. Stephan Günnemann

Professor, Technical University of Munich

Stephan Günnemann is a Professor in the department of Informatics, Technical University of Munich. Stephan Günnemann conducts research in the area of machine learning and data analytics. His main research focuses on how to make machine learning techniques reliable, thus, enabling their safe and robust use in various application domains. Prof. Günnemann is particularly interested in studying machine learning methods targeting complex data domains such as graphs/networks and temporal data.

Dr. Shuiwang Ji

Professor, Texas A&M University

Dr. Shuiwang Ji is currently a Professor and Presidential Impact Fellow in the Department of Computer Science & Engineering, Texas A&M University, directing the Data Integration, Visualization, and Exploration (DIVE) Laboratory. He received the Ph.D. degree in Computer Science from Arizona State University in 2010, advised by Prof. Jieping Ye. His research interests include machine learning for graphs, molecules, materials, and quantum systems. Dr. Ji received the National Science Foundation CAREER Award in 2014. Currently, he serves as an Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), ACM Transactions on Knowledge Discovery from Data (TKDD), and ACM Computing Surveys (CSUR). He regularly serves as an Area Chair or equivalent roles for AAAI Conference on Artificial Intelligence (AAAI), International Conference on Learning Representations (ICLR), International Conference on Machine Learning (ICML), International Joint Conference on Artificial Intelligence (IJCAI), ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), and Annual Conference on Neural Information Processing Systems (NeurIPS). Dr. Ji is a Fellow of AIMBE, a Distinguished Member of ACM, and a Senior Member of IEEE.

Dr. Haohan Wang

Assistant Professor, University of Illinois Urbana-Champaign

Dr. Wang is currently an Assistant Professor in UIUC. Dr. Wang's research focuses on the development of trustworthy machine learning methods for computational biology and healthcare applications, such as decoding the genomic language of Alzheimer's disease. In his work, he uses statistical analysis and deep learning methods, with an emphasis on data analysis using methods least influenced by spurious signals (features that are statistically associated with the target but not causal). In 2019, Wang was recognized as the Next Generation in Biomedicine by the Broad Institute of MIT and Harvard because of his contributions in dealing with confounding factors with deep learning.

Dr. Yinglong Xia

Applied Research Scientist, Meta

Dr. Yinglong Xia is an Uber TL in Meta, working on AI Relevance for Modern Recommendation System (MRS), where he collaborates with his team on applying top-notch machine learning technology on graph data to a series of products, such as the Instagram and Shop. He also drives the research in graph learning via academia collaboration, leading to extensive publications in top conferences/journals and patents. Prior to that, he was a chief architect in Futurewei Technologies, and a TL of graph reasoning framework in IBM TJ Watson Research Center. Yinglong publishes extensively with 70+ technical papers and 30+ patents. He serves as Associate Editor for IEEE TKDE and IEEE TBD, and also actively works as an organizer/SPC/TPC in several top conferences.

Dr. Marinka Zitnik

Assistant Professor, Harvard University

Dr. Marinka Zitnik is an Assistant Professor of Biomedical Informatics, Harvard Medical School. Marinka Zitnik investigates machine learning for science and medicine. Her methods leverage biomedical data at the scale of billions of interactions among millions of entities, blend machine learning with statistics and data science, and infuse biomedical knowledge into deep learning. Problems she investigates are motivated by network biology and medicine, genomics, drug discovery, and health. Dr. Zitnik's research vision is that in the future data science and artificial intelligence will be routinely used to give clinicians diagnostic recommendations; give scientists testable hypotheses they can confirm experimentally and offer them insights into safe and precise treatments; and give patients guidance on self-care, e.g., how to lead a healthy lifestyle and recognize disease early. To realize this vision, Dr. Zitnik develops methods to reason over rich interconnected data and translates the methods into solutions for biomedical problems.


Organzing Chairs


Jingrui He

Associate Professor

University of Illinois at Urbana-Champaign


Jian Kang

Ph.D. Student

University of Illinois at Urbana-Champaign


Bo Li

Assistant Professor

University of Illinois at Urbana-Champaign


Jian Pei


Duke University


Dawei Zhou

Assistant Professor
Virginia Tech
(Corresponding Organizer)

Publicity Chair


Shuaicheng Zhang

Ph.D. Student

Virginia Tech

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